---
title: "Moral Values Across Countries"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
source_code: embed
---
```{r setup, include=FALSE}
# global options
knitr::opts_chunk$set(echo = FALSE,
tidy = TRUE,
cache = FALSE,
message = FALSE,
error = FALSE,
warning = FALSE)
# packages
library(flexdashboard)
library(here)
library(rio)
library(tidyverse)
library(magrittr)
library(lme4)
library(lmerTest)
library(colorBlindness)
library(dotwhisker)
library(tidytext)
library(ggeffects)
library(see)
theme_set(theme_minimal()) # set theme
options(scipen=999) # remove scientific notation
```
```{r wrangling, include = FALSE}
# import data
df <- import(here("data", "ALL_MFQ30.csv"), # moral values, countries, & sex
setclass = "tbl_df") %>%
janitor::clean_names()
df_c <- import(here("data", "Data_S1_sec.csv"), # country-level variables
setclass = "tbl_df") %>%
janitor::clean_names()
# data wrangling
df %<>%
drop_na() %>%
mutate(
across(where(is.double), as.numeric),
across(where(is.character), as.factor),
sex = recode(sex,
`1` = "Male",
`0` = "Female",
.default = NA_character_),
indiv = rowMeans(
select(., harm_avg, fairness_avg) # individualizing moral foundations
),
bind = rowMeans(
select(., ingroup_avg:purity_avg) # binding moral foundations
)
)
# check data structure and variables
str(df)
# descriptive statistics by country
c_desc <-
df %>%
pivot_longer(cols = c(indiv, bind),
names_to = "vars",
values_to = "val"
) %>%
select(country, vars, val) %>%
group_by(country, vars) %>%
summarise(mean = mean(val, na.rm = TRUE),
sd = sd(val, na.rm = TRUE),
min = min(val, na.rm = TRUE),
max = max(val, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(vars = fct_recode(vars,
Individualizing = "indiv",
Binding = "bind"
)
)
# descriptive statistics by country and sex
c_s_desc <-
df %>%
filter(country != "Poland") %>% # Poland has missing data in sex.
pivot_longer(cols = c(indiv, bind),
names_to = "vars",
values_to = "val"
) %>%
group_by(country, sex, vars) %>%
summarise(mean = mean(val, na.rm = TRUE),
sd = sd(val, na.rm = TRUE),
min = min(val, na.rm = TRUE),
max = max(val, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(vars = fct_recode(vars,
Individualizing = "indiv",
Binding = "bind"
)
)
```
# Values X Country
Sidebar {.sidebar}
-----------------------------------------------------------------------
**Data**
Data used in this dashboard come from the second study of XX. It's a publicly available dataset, which can be downloaded from XX.
19 countries.
**Summary of the Visualizations**
The first set of plots (on this page) represents the average scores for individualizing and binding foundations across countries. Next, building on this plot, I add the sex variable into the mix.
The second set of plots visualize the effect of sex and several country-level variables on the aforementioned foundations.
*Country-level variables*
a
a
a
a
a
a
Column {data-width=600}
-----------------------------------------------------------------------
### Final Version
```{r}
gmeans <-
c_desc %>%
group_by(vars) %>%
summarise(m = mean(mean))
c_desc %>%
ggplot() +
geom_vline(data = gmeans,
aes(xintercept = m),
linetype = 2,
alpha = .6) +
geom_col(
aes(mean, reorder_within(country, mean, vars),
fill = country,
alpha = .9
)
) +
scale_y_reordered() +
scale_x_continuous(expand = c(0, 0)
) +
facet_wrap(~vars,
scales = "free_y",
ncol = 2) +
theme(
plot.title.position = "plot",
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
legend.position = "none",
axis.text.y = element_text(color = "black",
size = 11),
axis.text.x = element_text(color = "black",
size = 9),
axis.title = element_blank()
) +
labs(
title = "Endorsement of Individualizing and Binding Moral Values Across Countries",
caption = "Vertical lines represent the average of all countries."
)
```
Column {data-width=400}
-----------------------------------------------------------------------
### Initial version
```{r}
c_desc %>%
ggplot() +
geom_col(
aes(mean, country, fill = vars),
position = "dodge"
) +
theme(
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank()
) +
scale_x_continuous(expand = c(0, 0))
```
> This is the first version of the plot. There is a lot to work on here. Both axes and legend labels seem confusing. X-axis scale is also not complete. Also, it is hard to see any patterns without using facet_wrap and sorting the values.
### Revised version
```{r}
c_desc %>%
ggplot() +
geom_col(
aes(mean, reorder_within(country, mean, vars)
)
) +
scale_y_reordered() +
scale_x_continuous(expand = c(0, 0)
) +
facet_wrap(~vars,
scales = "free_y",
ncol = 2) +
theme(
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
axis.text.y = element_text(color = "black",
size = 11),
axis.text.x = element_text(color = "black",
size = 9),
axis.title = element_blank()
) +
labs(
title = "Endorsement of Individualizing and Binding Moral Values Across Countries"
)
```
> The revised version looks much better. You can see interesting patterns such as Spain's scores. The colors are terrible, though. Also, it'd be nice to see the grand mean to have a general reference category.
# Predictors of moral values
Sidebar {.sidebar}
-----------------------------------------------------------------------
**Model**
I conducted a multilevel regression for each of the moral values where sex was the Level 1 predictor, the country-level variables were the Level 2 predictors, country had a random intercept, and a random slope for sex was used.
*R code*:
lmer(indiv ~ sex +
pop_sex_ratio + individualism +
masculinity + gender_eqality +
human_development_index +
overall_life_satisfaction_index +
(sex | country),
data = df)
**Interpretation**
The plot describes the fixed effects for the models described above. The coefficients are unstandardized and represented by the dots. The lines represent the 95% confidence intervals.
Looking at the plot, we can see that sex, gender equality, and overall life satisfaction are the significant predictors of individualizing moral values; whereas, only gender equality is a significant predictor of binding moral values.
Column {data-width=600}
-----------------------------------------------------------------------
```{r MLM, include=FALSE}
# merge country-level data with the main dataset
df <- left_join(df,
select(df_c, country:overall_life_satisfaction_index),
by = "country")
# MLM
model_i <- lmer(indiv ~ sex + # level 1 predictor
# level 2 predictors:
pop_sex_ratio + individualism + masculinity +
gender_eqality + human_development_index +
overall_life_satisfaction_index +
(sex | country), # random slope for sex
# random intercept for country
data = df
)
model_b <- lmer(bind ~ sex + # level 1 predictor
# level 2 predictors:
pop_sex_ratio + individualism + masculinity +
gender_eqality + human_development_index +
overall_life_satisfaction_index +
(sex | country), # random slope for sex
# random intercept for country
data = df
)
sjPlot::tab_model(model_i)
sjPlot::tab_model(model_b)
# extract coefficients
m_i_fixed <-
broom.mixed::tidy(model_i) %>%
filter(effect == "fixed",
term != "(Intercept)") %>%
select(-c(effect, group)) %>%
mutate(
term = recode(term,
`sexMale` = "Sex",
`pop_sex_ratio` = "Population Sex Ratio",
`individualism` = "Individualism",
`masculinity` = "Masculinity",
`gender_eqality` = "Gender Equality",
`human_development_index` = "Human Development Index",
`overall_life_satisfaction_index` = "Overall Life Satisfaction Index"),
model = "Individualizing",
) %>%
relocate(model, term)
m_b_fixed <-
broom.mixed::tidy(model_b) %>%
filter(effect == "fixed",
term != "(Intercept)") %>%
select(-c(effect, group)) %>%
mutate(
term = recode(term,
`sexMale` = "Sex",
`pop_sex_ratio` = "Population Sex Ratio",
`individualism` = "Individualism",
`masculinity` = "Masculinity",
`gender_eqality` = "Gender Equality",
`human_development_index` = "Human Development Index",
`overall_life_satisfaction_index` = "Overall Life Satisfaction Index"),
model = "Binding",
) %>%
relocate(model, term)
both_ms <- bind_rows(m_i_fixed, m_b_fixed)
```
### Final Version
```{r}
dw_plot(both_ms,
dot_args = list(size = 2)
) +
ggtitle("Predicting moral values by sex and country-level predictors") +
xlab("Unstandardized Coefficient") +
geom_vline(xintercept = 0,
colour = "grey60",
linetype = 2) +
theme(plot.title = element_text(face = "bold", vjust = 3),
plot.title.position = "plot",
axis.text.y = element_text(color = "black",
size = 11),
legend.justification = c(0, 0),
legend.position = c(.65, .85),
legend.background = element_rect(colour = "grey80"),
legend.title = element_blank(),
panel.grid.major.y = element_blank()
) +
scale_x_continuous(n.breaks = 10) +
scale_color_manual(values = c("cornflowerblue", "#F8766D"))
```
Column {data-width=400}
-----------------------------------------------------------------------
### Initial version
```{r}
dw_plot(both_ms) +
ggtitle("Predicting moral values") +
xlab("Unstandardized Coefficient") +
geom_vline(xintercept = 0,
colour = "grey60",
linetype = 2)
```
> The initial dot-whisker plot for the fixed effects. Added a vertical line, which made the plot a bit easier to interpret. However, legend looks awful. Modifying the x axis should also help.
### Revised version
```{r}
dw_plot(both_ms,
dot_args = list(size = 2)
) +
ggtitle("Predicting moral values by sex and country-level predictors") +
xlab("Unstandardized Coefficient") +
geom_vline(xintercept = 0,
colour = "grey60",
linetype = 2) +
theme(plot.title = element_text(face = "bold"),
plot.title.position = "plot",
axis.text.y = element_text(color = "black",
size = 11),
legend.justification = c(0, 0),
legend.position = c(.74, .85),
legend.background = element_rect(colour = "grey80"),
legend.title.align = .5
) +
scale_x_continuous(n.breaks = 10) +
scale_color_manual(values = c("blue", "red"))
```
> Easier to see the points. The legend makes sense now, but still could be better. Colors can be improved. Also, there are too many grid lines.
# Predictors of moral values for each country
Column {data-width=600}
-----------------------------------------------------------------------
### Final Version
```{r}
```
Column {data-width=400}
-----------------------------------------------------------------------
### Initial version
```{r}
# run the models
model1 <- lmer(indiv ~ gender_eqality + (sex|country), data = df)
model2 <- lmer(bind ~ gender_eqality + (sex|country), data = df)
# extract the predicted values
predicted1 <-
ggpredict(model1,
terms = c("gender_eqality", "country"),
type = "re")
predicted2 <-
ggpredict(model2,
terms = c("gender_eqality", "country"),
type = "re")
# plot
ggpubr::ggarrange(
predicted1 %>%
ggplot(aes(x, predicted, color = group)) +
geom_line(size = 1),
predicted2 %>%
ggplot(aes(x, predicted, color = group)) +
geom_line(size = 1),
common.legend = TRUE
)
```
### Revised version
```{r}
```
